Sort by
Refine Your Search
-
[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
modelling from Project 1, we will conduct focus groups and interviews with communities in high- and low-risk zones to capture local knowledge, behaviours, and risk perceptions related to AMR. In parallel
-
I am an ARC Future (former DECRA) Fellow and lead the Structured Nanophotonics Group at Monash University. My research in nanophotonics explores the full potential and multi-dimensional nature of
-
development of machine learning techniques that may be used to triage, classify, and otherwise process material of a distressing nature (such as child exploitation material). It will involve the use of deep
-
development of machine learning techniques that may be used to triage, classify, and otherwise process material of a distressing nature (such as child exploitation material). It will involve the use of deep
-
and the Australian Federal Police, and researches the ethical application of AI theories and techniques to problems of interest to law enforcement agencies. The work of the lab is applied in nature
-
and the Australian Federal Police, and researches the ethical application of AI theories and techniques to problems of interest to law enforcement agencies. The work of the lab is applied in nature
-
question will be developed with the student and could focus on publishing, peer review, research funding, scientific careers, collaboration, or the norms governing AI use. Projects may also use data from
-
question will be developed with the student and could focus on publishing, peer review, research funding, scientific careers, collaboration, or the norms governing AI use. Projects may also use data from
-
edge data processing methods for mobile MRI scanners and testing in clinical enviroment. Required knowledge Excellent programming, image processing, optimisation background Affinity to work across
-
. Nowak. “Evolution of Cooperation in Stochastic Games.” Nature 559, no. 7713 (July 2018): 246–49. https://doi.org/10.1038/s41586-018-0277-x. [3] McNamara, John M. “Towards a Richer Evolutionary Game